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🚀 Course Goals
- Set organizational and operational boundaries, classify activity into Scope 1, 2 and 3, and reconcile every record to a control total. Convert activity data to tonnes of CO2e with full provenance: unit conversions, a factor register tied to a named file, sheet and row, and an independent recomputation that proves the result reproduces. Govern emission factors like code: publication year, licence, version, and a restatement path when a factor set changes under a closed inventory. Build a raw/validated/curated/evidence architecture with dbt models, blocking tests, data contracts and a lineage trace from a dashboard figure back to its source files.
📘 About This Course
A sustainability number is easy to produce and hard to defend. Anyone can multiply a
meter reading by a factor from a spreadsheet. The difficult questions come later: which
factor, from which publication year, under which licence? Is that figure measured,
estimated or modelled? If an auditor asks where it came from, can you walk back from the
published tonne to the invoice line, the unit conversion and the file hash — in front
of them, without a week's notice?
This course builds the platform that can answer those questions. You start from an empty
Linux lab and finish with a running, traceable system for one synthetic company,
Kestrel Cold Chain Ltd, carried end to end: organizational and operational
boundaries, Scope 1, 2 and 3 classification, activity data converted to tonnes of CO2e
with every factor traced to a named file, sheet and row, location-based and market-based
Scope 2, a raw/validated/curated/evidence lakehouse, dbt transformations with blocking
tests and lineage, executable data-quality rules that refuse a bad supplier export, factor
version governance and restatements, energy telemetry read straight from the kernel,
right-sized containers, a Software Carbon Intensity calculation with a declared boundary,
carbon-aware scheduling against a live public grid-intensity API, CI gates, KPI
dictionaries, targets and scenarios, dashboards with anti-greenwashing review, an assurance
-ready reporting package, and a capstone that catches a false reduction before it reaches a
decision.
Every lesson is a real session on a real Linux lab, not slides about one. You watch the
official download, the checksum, the install, the version check, the configuration, the
health check and the reset of every tool. Each lesson also contains a
controlled failure: factors from the wrong publication year are applied to
the reporting year, a landlord export arrives a thousand times too large, a duplicate
invoice line lands overnight, a stale forecast drives a scheduling decision, a chart with a
truncated axis is drafted for the public website. You see the symptom, read the evidence,
apply the smallest correction and rerun.
The discipline is the point. Measured, estimated and modelled values are labelled as what
they are and never quietly mixed. No factor, result or benefit is invented. Freed capacity
is not claimed as a carbon saving, and a lower bill is never used as a proxy for lower
emissions. Standards — GHG Protocol, IFRS S1 and S2, ESRS, GRI and the Green
Software Foundation's SCI specification — are paraphrased and cited with their edition
and access date, never reproduced.
Roughly 30 % of the course explains and 70 % demonstrates. The whole stack is
free and open source, runs on one machine without a GPU and without a paid cloud account,
and every company figure is synthetic. Real public data is used where it is genuinely
public: UK DESNZ conversion factors under the Open Government Licence, and the NESO
Carbon Intensity API under CC BY 4.0. AWS appears in exactly one clearly marked
optional lesson, and the other 47 state that AWS was not used.
This course is educational material. It is not legal, audit, assurance, investment or
certification advice.
What You'll Learn
Set organizational and operational boundaries, classify activity into Scope 1, 2 and 3, and reconcile every record to a control total.
Convert activity data to tonnes of CO2e with full provenance: unit conversions, a factor register tied to a named file, sheet and row, and an independent recomputation that proves the result reproduces.
Govern emission factors like code: publication year, licence, version, and a restatement path when a factor set changes under a closed inventory.
Build a raw/validated/curated/evidence architecture with dbt models, blocking tests, data contracts and a lineage trace from a dashboard figure back to its source files.
Make data quality executable with Pandera and a rule register that ranks findings by the tonnes at stake and refuses to publish a bad dataset.
Measure the systems themselves: Linux energy telemetry, right-sized containers, profiling, a Software Carbon Intensity calculation with a declared boundary, and carbon-aware scheduling against a live grid-intensity API.
Design KPIs, targets and transition scenarios, and run a dashboard review that catches truncated axes, uncovered scopes and unannotated method changes before publication.
Assemble an assurance-ready reporting package: internal controls, evidence manifests, data ethics, regulatory change tracking, and a handover a reviewer can actually follow.
🛠️ Requirements
- Comfortable on the Linux command line, with basic SQL and Python; no prior carbon-accounting or ESG-reporting experience is assumed.
- A machine or VM with about 8 GB of RAM, a few CPU cores and 25 GB of free disk, running Ubuntu 22.04 or 24.04.
- No GPU and no paid cloud account: the reference stack is free and open source, and AWS appears only in one clearly marked optional lesson.
- Everything else is installed on camera from the official source, with a checksum, a version check and an uninstall path.
👤 Who This Course is For
- Data and analytics engineers asked to build the pipeline behind a sustainability report, and who need it to survive an audit question.
- Sustainability, ESG and carbon-accounting professionals who want to understand and challenge the system producing their numbers.
- Software, platform and cloud engineers measuring the footprint of their own services, and anyone implementing Software Carbon Intensity.
- Internal audit, assurance and controls people preparing for ESG assurance, and technical leads who have to review a reporting platform.
53618 Students applied
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
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John Doe
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet
Morbi nec nisi ante. Quisque lacus ligula, iaculis in elit et, interdum semper quam. Fusce in interdum tortor. Ut sollicitudin lectus dolor eget imperdiet libero pulvinar sit amet